A
Anil Kumar
Researcher at Amity University
Publications - 79
Citations - 1732
Anil Kumar is an academic researcher from Amity University. The author has contributed to research in topics: Computer science & Fault (power engineering). The author has an hindex of 18, co-authored 71 publications receiving 712 citations. Previous affiliations of Anil Kumar include Sant Longowal Institute of Engineering and Technology & Wenzhou University.
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Latest developments in gear defect diagnosis and prognosis: A review
TL;DR: An insight into various defects that generally occur in gears is provided and a state-of-the-art review is provided on the latest and most widely used diagnosis methods for gearbox condition monitoring.
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Fault diagnosis of angle grinders and electric impact drills using acoustic signals
Adam Glowacz,Ryszard Tadeusiewicz,Stanisław Legutko,Wahyu Caesarendra,Muhammad Irfan,Hui Liu,Frantisek Brumercik,Miroslav Gutten,Maciej Sułowicz,Jose Alfonso Antonino Daviu,Thompson Sarkodie-Gyan,Pawel Fracz,Anil Kumar,Jiawei Xiang +13 more
TL;DR: The authors proposed a method for feature extraction: SMOFS-NFC (Shortened Method of Frequencies Selection Nearest Frequency Components), which is very useful for diagnosis of bearings, ventilation faults and other mechanical faults of power tools.
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Novel Convolutional Neural Network (NCNN) for the Diagnosis of Bearing Defects in Rotary Machinery
TL;DR: In this article, a novel trigonometric cross-entropy function is developed to compute the sparsity cost, which introduces sparsity by avoiding unnecessary activation of neurons in the hidden layers of CNN.
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Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images
TL;DR: Experimental results achieved while diagnosis defects of centrifugal pump show that the proposed improved CNN has a significant improvement of identification accuracy of about 3.2% over traditional CNN.
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Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump
TL;DR: In this article, a robust automated signal processing algorithm is proposed for defect identification in the centrifugal pump, where features sensitive to defective conditions are extracted from raw signal and scale marginal integration graph.